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Title: :  IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL
PaperId: :  26476
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26476
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
S. Mahammad ArifKV SUBBA REDDY ENGINEERING COLLEGE
G.Arun KumarKV SUBBA REDDY ENGINEERING COLLEGE
S. SyesavaliKV SUBBA REDDY ENGINEERING COLLEGE
K. LokeshKV SUBBA REDDY ENGINEERING COLLEGE
H ATEEQ AHMED KV SUBBA REDDY ENGINEERING COLLEGE

Abstract

COMPUTER SCIENCE AND ENGINEERING
Diabetes Prediction, Machine Learning, KNN Imputation, Tri-Ensemble Model, Healthcare Analytics, Data Preprocessing.
Diabetes Mellitus (DM) is a prevalent chronic disease that poses significant health and economic burdens worldwide. Early detection and accurate prediction of diabetes are crucial for timely intervention and improved patient outcomes. However, real-world medical datasets often contain missing values, which can adversely impact the performance of machine learning models. This study proposes an automated diabetes prediction framework that integrates K-Nearest Neighbors (KNN) imputation for handling missing data and a novel Tri-Ensemble Model for enhanced classification accuracy. The proposed Tri-Ensemble Model combines Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Trees Classifier (ETC) using a voting mechanism to make robust predictions. The dataset, sourced from the Pima Indians Diabetes Database, was preprocessed to address missing values using KNN imputation, and the models were trained using a 70:30 train-test split. The experimental results demonstrate that the proposed Tri-Ensemble Model significantly outperforms traditional machine learning classifiers. With an accuracy of 97.49%, precision of 98.16%, recall of 99.35%, and F1-score of 98.84%, the model surpasses existing state-of-the-art approaches. Additionally, a comparative analysis reveals that imputing missing values using KNN substantially improves model performance compared to deleting missing data. The findings of this research highlight the importance of effective data preprocessing and ensemble learning techniques in medical diagnostics. The proposed model holds promise for real-world healthcare applications, facilitating early diabetes detection and improving patient care. Future research will explore deep learning models to further enhance predictive accuracy in diabetes diagnosis.

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IJARIIE S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . "IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 466-473
MLA S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . "IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 466-473.
APA S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, & H ATEEQ AHMED . (2025). IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 466-473.
Chicago S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . "IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 466-473.
Oxford S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . 'IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 466-473. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/IMPROVING_HEALTHCARE_PREDICTION_OF_DIABETIC_PATIENTS_USING_KNN_IMPUTED_FEATURES_AND_TRI_ENSEMBLE_MODEL_ijariie26476.pdf (Accessed : ).
Harvard S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . (2025) 'IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 466-473IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/IMPROVING_HEALTHCARE_PREDICTION_OF_DIABETIC_PATIENTS_USING_KNN_IMPUTED_FEATURES_AND_TRI_ENSEMBLE_MODEL_ijariie26476.pdf (Accessed : )
IEEE S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED , "IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 466-473, May-Jun 2025. [Online]. Available: http://ijariie.com/AdminUploadPdf/IMPROVING_HEALTHCARE_PREDICTION_OF_DIABETIC_PATIENTS_USING_KNN_IMPUTED_FEATURES_AND_TRI_ENSEMBLE_MODEL_ijariie26476.pdf [Accessed : ].
Turabian S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . "IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver S. Mahammad Arif, G.Arun Kumar, S. Syesavali, K. Lokesh, and H ATEEQ AHMED . IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODEL. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 466-473. Available from: http://ijariie.com/AdminUploadPdf/IMPROVING_HEALTHCARE_PREDICTION_OF_DIABETIC_PATIENTS_USING_KNN_IMPUTED_FEATURES_AND_TRI_ENSEMBLE_MODEL_ijariie26476.pdf
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